Author(s): Alok Ranjan Singh

Originally published on Towards AI.

Understanding A2A, agent communication protocols, and the future of distributed AI systems

Most teams aren’t struggling to build AI agents anymore.

They’re struggling to live with the ones they already built.

The same summarization agent exists in five repositories. The same RAG pipeline behaves slightly differently across products. Improvements made in one place never make it to the others.Prompts drift. Guardrails diverge. Observability becomes fragmented.

And slowly, what looked like progress turns into operational friction.

The real bottleneck in AI systems today, isn’t intelligence — it’s architecture.

The Problem Nobody Talks About in Agent Systems

The first wave of GenAI adoption followed a predictable pattern:

• Build an agent.
• It works.
• Another team needs it.
• Copy the code.

Repeat.

Early on, this feels productive. Shipping speed is high. Experiments move fast. But as systems grow, hidden costs start appearing:

• Prompt divergence across teams
• Inconsistent outputs for identical tasks
• Security and governance duplication
• Multiple deployment pipelines for the same capability
• Difficult upgrades when models change

Agents become code artifacts instead of reusable capabilities.

This is the same problem backend engineering faced before microservices became mainstream. The issue wasn’t logic — it was coupling.

And AI is now rediscovering that lesson.

The Shift: Agents Are Becoming Services

A subtle but important shift is happening in how serious AI systems are being designed.

We are moving from:

Application → Local Agent

to:

Agent → Remote Agent → Specialized Capability

Instead of embedding intelligence everywhere, teams are beginning to expose intelligence as reusable services.

Build once. Deploy once. Reuse everywhere.

This is where the combination of Agent Development Kits (ADK) and the Agent-to-Agent (A2A) protocol becomes interesting.

The idea is simple:

• Build an agent once.
• Deploy it as a remote capability.
• Allow other agents to discover and use it safely.

Not unlike how REST standardized service communication years ago.

What A2A Actually Solves (And Why It Matters)

As soon as organizations started building multiple agents, a new problem emerged:

Every framework invented its own integration logic.

Different message formats. Different discovery mechanisms. Different assumptions about execution.

In other words — no shared language.

The A2A Protocol specification introduces a standard way for agents to:

• discover each other,
• understand capabilities,
• communicate through structured messages,
• and collaborate without tight coupling.

In simple terms:

A2A is a communication contract for AI agents.

It allows independently built agents to interact without knowing each other’s internal implementation.

And that changes how systems scale.

Going One Level Deeper — The Architecture Behind It

Let’s remove the abstraction and look at the moving pieces.

1️⃣ The Remote Agent (A2A Server)

A remote agent exposes a capability through a standard interface.

Examples:

• Retrieval agent
• Illustration agent
• Code review agent
• Domain-specific analysis agent

Internally, it can use any model, framework, or toolchain.

Externally, it speaks the protocol.

This separation is critical because it allows implementation to evolve independently from usage.

2️⃣ The Agent Card — The Missing Abstraction

The Agent Card is where things become powerful.

It describes:

• agent identity
• capabilities
• input/output expectations
• authentication requirements
• service endpoints

Think of it as:

OpenAPI — but for AI agents.

Other agents read this metadata before interacting. No hardcoded integrations. No implicit assumptions.

From a systems perspective, this is what enables discoverability and composability at scale.

Research exploring secure implementations of A2A also highlights the Agent Card as a critical element for identity, capability declaration, and safe interaction between agents.

3️⃣ The Client Agent (Orchestrator)

The calling agent:

• reads the Agent Card,
• determines capability fit,
• sends a structured task,
• integrates the response into a larger workflow.

The caller does not need to know:

• which model is used,
• how prompts are structured,
• or how execution happens internally.

This creates true decoupling between intelligence and orchestration.

Why This Matters Technically

Without a protocol:

Agent A ↔ Custom Integration ↔ Agent B

With A2A:

Agent A ↔ Standard Protocol ↔ Agent B

The difference seems small until systems grow.

Integration complexity stops scaling exponentially.

Teams stop rebuilding capabilities and start composing them.

And composition is what actually scales engineering organizations.

This isn’t just an industry observation. Research is beginning to describe the same shift.

What Research Is Saying About Agent Protocols

This shift isn’t just happening in industry.

Recent academic work analyzing emerging agent protocols highlights the same limitation — the absence of standardized communication makes interoperability and large-scale collaboration between agents difficult, ultimately limiting the complexity of problems agents can solve.

A comprehensive survey of agent protocols explores how standardization could enable collaborative intelligence across distributed systems.

👉 Full paper: Survey of AI Agent Protocols

Beyond Engineering Convenience — Why Research Is Moving Here

This shift isn’t just industry experimentation.

Academic and standards work is converging in the same direction.

Recent research on agent communication emphasizes that as multi-agent systems grow, standardized communication becomes foundational to reliability and performance.

Security-focused research around A2A further shows that protocol-level guarantees — identity, authentication, and structured task execution — become essential once agents interact across organizational boundaries.

At the standards level, the IETF draft on AI agent protocol frameworks explores how multiple protocols (including A2A and MCP) fit into a broader internet-scale communication model for AI agents.

This is a strong signal:

We are moving from agent experiments to agent infrastructure.

And this direction is now reaching standards discussions as well.

Where Standards Bodies Are Heading Next

At the standards level, early work within the IETF community is already exploring framework requirements for interoperable AI agent protocols — including identity, communication models, and cross-system coordination.

This signals that agent communication is gradually moving from experimental architecture toward internet-scale infrastructure design.

👉 IETF Draft — AI Protocol Framework

The Bigger Architectural Pattern Emerging

If you zoom out, something familiar appears.

We already solved similar problems once:

| Era | Problem | Solution |
| ------------------- | ---------------------- | --------------------- |
| Monoliths | Tight coupling | Microservices |
| APIs | Integration chaos | REST/OpenAPI |
| Cloud systems | Scaling complexity | Service orchestration |
| Agent systems (now) | Capability duplication | Agent protocols |

The industry is rediscovering an old truth:

Scaling intelligence is easier than scaling coordination.

Better models help.
Clear interfaces scale.

Real Engineering Benefits (When Done Right)

When agents become remote capabilities instead of embedded logic:

✅ One deployment serves multiple teams
✅ Centralized safety and governance
✅ Versioned agent capabilities
✅ Easier model upgrades
✅ Cleaner observability boundaries
✅ Reduced operational drift

Most importantly:

Teams start composing systems instead of rebuilding them.

A Note of Appreciation

The A2A ecosystem exists because of strong open collaboration.

Thanks to the contributors pushing the protocol and implementation forward, including Holt S., Darrel Miller, Luca Muscariello, Amye Scavarda Perrin and the broader open-source community contributing to interoperability in AI systems.

The protocol specification is available at the A2A Protocol website, and the reference implementation can be explored in the A2A GitHub repository.

• A2A Protocol website → https://a2a-protocol.org
• A2A GitHub repository → https://github.com/a2aproject/A2A

Where This Is Heading

We’re moving toward multi-agent systems where:

• specialized agents focus on narrow responsibilities,
• orchestration agents coordinate workflows,
• and protocols handle interoperability.

The challenge is no longer building smarter agents.

It’s making agents work together reliably.

The teams that recognize this early won’t just build better agents — they’ll build systems that improve faster over time.

Because once intelligence becomes reusable infrastructure, innovation stops being linear.

It compounds.

A Question Worth Thinking About

How many agents in your organization today exist more than once — slightly different, slightly incompatible, quietly diverging?

How many of them could instead become reusable capabilities?

The next leap in AI systems probably won’t come from bigger models.

It will come from better architecture.

And the teams that treat agents as infrastructure — not features — will be the ones that move fastest when everything else catches up.

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Published via Towards AI